U.S. patent application number 16/999912 was filed with the patent office on 2022-02-24 for accelerometer inside of a microphone unit.
The applicant listed for this patent is WAYMO LLC. Invention is credited to Choon Ping Chng, Dennis Wu.
Application Number | 20220059118 16/999912 |
Document ID | / |
Family ID | 1000005088003 |
Filed Date | 2022-02-24 |
United States Patent
Application |
20220059118 |
Kind Code |
A1 |
Chng; Choon Ping ; et
al. |
February 24, 2022 |
Accelerometer Inside of a Microphone Unit
Abstract
A system includes a microphone unit coupled to a roof of an
autonomous vehicle. The microphone unit includes a microphone board
having a first opening. The microphone unit also includes a first
microphone positioned over the first opening and coupled to the
microphone board. The microphone unit further includes an
accelerometer. The system also includes a processor coupled to the
microphone unit.
Inventors: |
Chng; Choon Ping; (Los
Altos, CA) ; Wu; Dennis; (Mountain View, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
WAYMO LLC |
MOUNTAIN VIEW |
CA |
US |
|
|
Family ID: |
1000005088003 |
Appl. No.: |
16/999912 |
Filed: |
August 21, 2020 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B60R 11/02 20130101;
H04R 3/005 20130101; H04R 1/08 20130101; G10L 25/51 20130101; B60R
2011/004 20130101; H04R 1/406 20130101; H04R 2499/13 20130101; G10K
11/17873 20180101; G10K 2210/3027 20130101; G10K 2210/12821
20130101; G01P 15/0907 20130101 |
International
Class: |
G10L 25/51 20060101
G10L025/51; G01P 15/09 20060101 G01P015/09; G10K 11/178 20060101
G10K011/178; H04R 1/08 20060101 H04R001/08; H04R 1/40 20060101
H04R001/40; H04R 3/00 20060101 H04R003/00; B60R 11/02 20060101
B60R011/02 |
Claims
1. A system comprising: a microphone unit coupled to a roof of an
autonomous vehicle, the microphone unit comprising: a microphone
board having a first opening; a first microphone positioned over
the first opening and coupled to the microphone board; and an
accelerometer, wherein the accelerometer measures vibrations
proximate to the microphone unit and generates an electrical signal
indicative of a waveform associated with the measured vibrations,
and wherein the measured vibrations are caused by sounds external
to the autonomous vehicle; and a processor coupled to the
microphone unit.
2. (canceled)
3. The system of claim 1, wherein the processor is configured to
determine audio characteristics of the sounds external to the
autonomous vehicle based on the electrical signal in response to a
determination that the first microphone is failing to detect the
sounds external to the autonomous vehicle.
4. The system of claim 3, wherein the sounds external to the
autonomous vehicle correspond to a siren.
5. The system of claim 1, wherein the measured vibrations are
caused by wind noise, and wherein the processor is configured to
generate a noise cancellation signal based on the electrical
signal.
6. The system of claim 1, wherein the processor is configured to
monitor the electrical signal over a particular distance travelled
by the autonomous vehicle.
7. The system of claim 6, wherein the processor is configured to
determine that the monitored vibrations are caused by environmental
noise in response to a determination that the electrical signal
indicates the waveform is not substantially continuous over the
particular distance travelled by the autonomous vehicle.
8. (canceled)
9. The system of claim 1, wherein the microphone board has a second
opening and a third opening, and wherein the microphone unit
further comprises: a second microphone positioned over the second
opening and coupled to the microphone board; and a third microphone
positioned over the third opening and coupled to the microphone
board.
10. The system of claim 9, wherein the first microphone, the second
microphone, and the third microphone are proximate to an edge of
the microphone board.
11. The system of claim 10, wherein the first microphone is
oriented in a first direction, wherein the second microphone is
oriented in a second direction that is 120 degrees from the first
direction, and wherein the third microphone is oriented in a third
direction that is 120 degrees from the first direction and 120
degrees from the second direction.
12. A method comprising: receiving, at a processor, an electrical
signal generated by an accelerometer, the accelerometer included in
a microphone unit that is coupled to a roof of an autonomous
vehicle, and the electrical signal indicative of a waveform
associated with vibrations measured by the accelerometer, wherein
the measured vibrations are caused by sounds external to the
autonomous vehicle; and determining a cause of the vibrations based
on the electrical signal.
13. The method of claim 12, wherein determining the cause of the
vibrations comprises: monitoring the electrical signal over a
particular distance travelled by the autonomous vehicle; and
determining that the cause of the vibrations is environmental noise
in response to a determination that the electrical signal indicates
the waveform is not substantially continuous over the particular
distance travelled by the autonomous vehicle.
14. (canceled)
15. (canceled)
16. The method of claim 12, wherein the sounds external to the
autonomous vehicle correspond to a siren.
17. The method of claim 12, further comprising: determining that
the cause of the vibrations is wind noise; and generating a noise
cancellation signal based on the electrical signal to substantially
reduce the wind noise.
18. A non-transitory computer-readable medium having stored therein
instructions executable by a computing device to cause the
computing device to perform functions, the functions comprising:
receiving, at a processor, an electrical signal generated by an
accelerometer, the accelerometer included in a microphone unit that
is coupled to a roof of an autonomous vehicle, and the electrical
signal indicative of a waveform associated with vibrations measured
by the accelerometer, wherein the measured vibrations are caused by
sounds external to the autonomous vehicle; and determining a cause
of the vibrations based on the electrical signal.
19. (canceled)
20. The non-transitory computer-readable medium of claim 18,
wherein the functions further comprise: monitoring a signature
associated with the accelerometer, the signature based on the
vibrations; feeding the signature into a machine learning
algorithm.
Description
BACKGROUND
[0001] Autonomous vehicles or vehicles operating in an autonomous
mode may encounter scenarios in which maneuvers may be undertaken
quickly based on unanticipated changes in a surrounding
environment. As a non-limiting example, if an emergency vehicle
turns on a siren, an autonomous vehicle may responsively steer to
the side of the road and stop.
[0002] Typically, an autonomous vehicle uses sensors to determine
its surrounding environment. For example, the autonomous vehicle
could use light detection and ranging (LIDAR) devices, radio
detection and ranging (RADAR) devices, and/or cameras to capture
data of the environment surrounding the autonomous vehicle.
However, in some instances, objects may not be readily detected by
such sensors, such as when objects are outside of the fields of
view of the sensors or when portions of the fields of view of the
sensors are blocked (e.g., by buildings, other vehicles,
vegetation, etc.). In such instances, the autonomous vehicle may
not be able to determine aspects of its surrounding
environment.
SUMMARY
[0003] The present disclosure generally relates to using an
accelerometer in a microphone unit that is mounted on an autonomous
vehicle (e.g., positioned on a roof of an autonomous vehicle) to
detect low frequency vibrations.
[0004] In a first aspect, a system includes a microphone unit
coupled to a roof of an autonomous vehicle. The microphone unit
includes a microphone board having a first opening. The microphone
unit also includes a first microphone positioned over the first
opening and coupled to the microphone board. The microphone unit
further includes an accelerometer. The system also includes a
processor coupled to the microphone unit.
[0005] In a second aspect, a method includes receiving, at a
processor, an electrical signal generated by an accelerometer. The
accelerometer is included in a microphone unit that is coupled to a
roof of an autonomous vehicle. The electrical signal is indicative
of a waveform associated with vibrations proximate to the
microphone unit that are measured by the accelerometer. The method
further includes determining a cause of the vibrations based on the
electrical signal.
[0006] In a third aspect, a non-transitory computer-readable medium
stores instructions executable by a computing device to cause the
computing device to perform functions. The functions include
receiving an electrical signal generated by an accelerometer. The
accelerometer is included in a microphone unit that is coupled to a
roof of an autonomous vehicle. The electrical signal is indicative
of a waveform associated with vibrations proximate to the
microphone unit that are measured by the accelerometer. The
functions also include determining a cause of the vibrations based
on the electrical signal.
[0007] Other aspects, embodiments, and implementations will become
apparent to those of ordinary skill in the art by reading the
following detailed description, with reference where appropriate to
the accompanying drawings.
BRIEF DESCRIPTION OF THE FIGURES
[0008] FIG. 1 is a functional diagram illustrating components of an
autonomous vehicle, in accordance with an example embodiment.
[0009] FIG. 2 is a functional diagram illustrating an
accelerometer, in accordance with an example embodiment.
[0010] FIG. 3 depicts a diagram of microphones and an
accelerometer, in accordance with an example embodiment.
[0011] FIG. 4 depicts a diagram of different roof locations to
couple a microphone unit, in accordance with example
embodiments.
[0012] FIG. 5 is a flowchart of a method, according to an example
embodiment.
DETAILED DESCRIPTION
[0013] Example methods, devices, and systems are described herein.
It should be understood that the words "example" and "exemplary"
are used herein to mean "serving as an example, instance, or
illustration." Any embodiment or feature described herein as being
an "example" or "exemplary" is not necessarily to be construed as
preferred or advantageous over other embodiments or features. Other
embodiments can be utilized, and other changes can be made, without
departing from the scope of the subject matter presented
herein.
[0014] Thus, the example embodiments described herein are not meant
to be limiting. Aspects of the present disclosure, as generally
described herein, and illustrated in the figures, can be arranged,
substituted, combined, separated, and designed in a wide variety of
different configurations, all of which are contemplated herein.
[0015] Further, unless context suggests otherwise, the features
illustrated in each of the figures may be used in combination with
one another. Thus, the figures should be generally viewed as
component aspects of one or more overall embodiments, with the
understanding that not all illustrated features are necessary for
each embodiment.
I. Overview
[0016] The present disclosure generally relates to using an
accelerometer inside of a microphone unit that is coupled to an
autonomous vehicle (e.g., coupled to a roof of an autonomous
vehicle). The microphone unit can additionally include one or more
microphones to detect environmental sounds, such as sirens from
emergency vehicles. Advantageously, the accelerometer can detect
low frequency vibrations (e.g., sounds) that may be difficult for
the microphones to detect or that are a source of noise for the
microphones. For example, the accelerometer can measure vibrations
that are generated based on low frequency sounds, such as sirens,
and generate an output voltage (e.g., an electrical signal) having
a waveform that is indicative of the measured vibrations. A
computing system determines sound frequencies associated with the
output voltage of the accelerometer and identifies a source of the
low frequency sounds based on the sound frequencies. As a
non-limiting example, the computer system can determine that the
source of the low frequency sound is an ambulance siren if the
sound frequencies fall within the frequency range of a typical
ambulance siren.
[0017] Additionally, in scenarios where the low frequency sounds
correspond to noise, such as wind noise, the computing system can
generate a noise cancellation signal to reduce the noise at the
microphone unit. For example, upon determining the sound frequency
associated with the output voltage of the accelerometer, the
computing system can generate a noise cancellation signal to
substantially reduce the noise at the microphone unit.
II. Example Embodiments
[0018] FIG. 1 is a functional diagram illustrating components of an
autonomous vehicle 100 in accordance with an example embodiment.
The autonomous vehicle 100 may take the form of a car, truck,
motorcycle, bus, boat, airplane, helicopter, lawn mower, earth
mover, snowmobile, aircraft, recreational vehicle, amusement park
vehicle, farm equipment, construction equipment, tram, golf cart,
train, and trolley, for example. Other vehicles are possible as
well. The autonomous vehicle 100 may be configured to operate fully
or partially in an autonomous mode. For example, the autonomous
vehicle 100 may control itself while in the autonomous mode, and
may be operable to determine a current state of the autonomous
vehicle 100 and its environment, determine a predicted behavior of
at least one other vehicle in the environment, determine a
confidence level that may correspond to a likelihood of the at
least one other vehicle to perform the predicted behavior, and
control the autonomous vehicle 100 based on the determined
information. While in the autonomous mode, the autonomous vehicle
100 may be configured to operate without human interaction.
[0019] In FIG. 1, a roof 102 of the autonomous vehicle 100 is
shown. A microphone unit 150 is coupled to the roof 102 of the
autonomous vehicle 100. Although one microphone unit 150 is
illustrated in FIG. 1, in other implementations, a plurality of
microphone units having similar configurations as the microphone
unit 150 can be coupled to the roof 102 of the autonomous vehicle
100. For example, in one implementation, three microphone units can
be coupled to the roof 102 of the autonomous vehicle 100 at various
locations. In another implementation, two microphone units can be
coupled to the roof 102 of the autonomous vehicle 100 at various
locations.
[0020] The microphone unit 150 includes a microphone board 157 that
is positioned on top of fur 160. Although described as fur 160, in
other implementations, other materials can be used as a means to
situate the microphone board 157. The microphone board 157 has a
first opening 153A, a second opening 153B, and a third opening
153C. As used herein, the microphone openings 153A-153C can also be
referred to as "microphone cavities." A first microphone 151A is
positioned over the first opening 153A and is coupled to the
microphone board 157. The first opening 153A is sealed by a first
protective vent 152A that enables air to pass through the first
opening 153A. A second microphone 151B is positioned over the
second opening 153B and is coupled to the microphone board 157. The
second opening 153B is sealed by a second protective vent 152B that
enables air to pass through the second opening 153B. A third
microphone 151C is positioned over the third opening 153C and is
coupled to the microphone board 157. The third opening 153C is
sealed by a third protective vent 152C that enables air to pass
through the third opening 153C. The microphones 151A-151C are
configured to detect sounds, such as a low frequency sound 190, and
generate audio frames based on the detected sounds.
[0021] An accelerometer 200 is also coupled to the microphone board
157. The accelerometer 200 is configured to measure vibrations 260
proximate to the microphone unit 150 and generate an electrical
signal 262 indicative of a waveform (e.g., a voltage waveform)
associated with the measured vibrations 260. A non-limiting example
of the accelerometer 200 is depicted in FIG. 2. For example, FIG. 2
illustrates a piezoelectric accelerometer 200 that is configured to
measure the vibrations 260 proximate to the microphone unit 150. It
should be understood that the techniques described herein can be
implemented with a variety of different accelerometers, such as
capacitance accelerometers, thermal accelerometers, gyroscopes,
etc. Thus, the piezoelectric accelerometer 200 illustrated and
described with respect to FIG. 2 should not be construed as
limiting.
[0022] In FIG. 2, a structure of the accelerometer 200 is defined
by a housing 242. The accelerometer 200 includes an electrode 244
that is positioned proximate to a bottom of the housing 242, a
piezoelectric material 246 that is positioned on top of the
electrode 244, and an electrode 248 that is positioned on top of
the piezoelectric material 246. A mass 250 is positioned on top of
the electrode 248, and a spring plate 252 is positioned on top of
the mass 250.
[0023] During operation, the vibrations 260 can cause a force to be
applied to the piezoelectric material 246. For example, the
vibrations 260 can cause the spring plate 252 to repeatedly
compress and decompress, which in turn, causes the mass 250 to
apply pressure and force to the piezoelectric material 246. Based
on the piezoelectric effect, the mechanical stress applied to the
piezoelectric material 246 during the compression and decompression
of the spring plate 252 can cause the piezoelectric material 246 to
generate an electric charge that is indicative of an output voltage
(V.sub.out). For example, the output voltage (V.sub.out) is based
on a voltage difference between a terminal 254 coupled to the
electrode 244 and a terminal 256 coupled to the electrode 248. The
electric signal 262 is indicative of, or representative of, the
output voltage (V.sub.out).
[0024] According to some implementations, the accelerometer 200
converts the voltage difference between the terminals 254, 256 to a
digital signal. For example, the accelerometer 200 can include
analog-to-digital conversion registers that convert analog signals
reflective of the output voltage (V.sub.out) to digital signals
that are reflective of the output voltage (V.sub.out). According to
this implementation, the electrical signal 262 can be a digital
signal that is indicative of the output voltage (V.sub.out).
[0025] Thus, the electrical signal 262 (e.g., the output voltage
(V.sub.out)) is indicative of a waveform associated with the
measured vibrations 260. The vibrations 260 can be caused by one or
more of a plurality of factors, such as low frequency sounds, wind
noise, faulty connections associated with the autonomous vehicle
100, etc.
[0026] In example embodiments, as the sound frequency that causes
the vibrations 260 increases, the output voltage (V.sub.out) also
increases. As a non-limiting example, a sound having a frequency of
100 Hertz (Hz) can result in vibrations 260 that cause the
accelerometer 200 to generate an output voltage (V.sub.out) of 5
volts, a sound having a frequency of 200 Hz can result in increased
vibrations 260 that cause the accelerometer 200 to generate an
output voltage (V.sub.out) of 10 volts, etc. Thus, as described
below, the output voltage (V.sub.out) associated with the
electrical signal 262 can be used to determine a frequency of
surrounding sounds.
[0027] Referring back to FIG. 1, the electrical signal 262
generated by the accelerometer 200 can be transmitted to the
microphone board 157, and the microphone board 157 can transmit the
electrical signal 262 to a connector board 158 via a board-to-board
connector 162. The electrical signal 262 can be used to determine a
cause (e.g., a sound source) of the measured vibrations 260
proximate to the microphone unit 150. For example, as described
below, the electrical signal 262 can be used to determine whether
the vibrations 260 are caused by wind noise, other environmental
noise, a faulty connection associated with the autonomous vehicle
100, etc.
[0028] The electrical signal 262 is provided to a computing system
110. For example, a bus can transmit the electrical signal 262 from
the connector board 158 to the computing system 110. The bus can be
a wired connection or a wireless communication medium that is used
to communicate messages and signals between the microphone unit 150
and the computing system 110. As shown in FIG. 1, the computing
system 110 can be integrated into a cabin 103 of the autonomous
vehicle 100. For example, the computing system 110 can be
integrated into a front console or a center console of the
autonomous vehicle 110.
[0029] The computing system 110 includes a processor 112 that is
coupled to a memory 114. The memory 114 can be a non-transitory
computer-readable medium that stores instructions 124 that are
executable by the processor 112. The processor 112 includes an
accelerometer processing module 116, a microphone processing module
118, and a noise cancellation module 120. According to some
implementations, one or more of the modules 116, 118, 120 can
correspond to software (e.g., instructions 124) executable by the
processor 112. According to other implementations, one or more the
modules 116, 118, 120 can correspond to dedicated circuitry (e.g.,
application-specific integrated circuits (ASICs) or field
programmable gate arrays (FPGAs)) integrated into the processor
112.
[0030] Based on the electric signal 262, the accelerometer
processing module 116 is configured to determine the cause of the
vibrations 260. To illustrate, the accelerometer processing module
116 can access waveform voltage data 126 from the memory 114. The
waveform voltage data 126 is usable by the accelerometer processing
module 116 to translate different output voltage (V.sub.out)
waveforms of the accelerometer 200 into corresponding frequency
ranges. For example, the waveform voltage data 126 can indicate
different sound frequency ranges for an output voltage (V.sub.out)
waveform generated by the accelerometer 200. Thus, using the
waveform voltage data 126, the accelerometer processing module 116
can perform a look-up operation to identify a frequency range of a
sound that results in the accelerometer 200 producing a specified
output voltage (V.sub.out).
[0031] To illustrate, the accelerometer processing module 116 can
perform the look-up operation based on the output voltage
(V.sub.out) indicated by the electrical signal 262. As a
non-limiting example, assume that the electrical signal 262
indicates that the waveform of the output voltage (V.sub.out) spans
from a lower-end voltage (e.g., 2.2 volts) to a higher-end voltage
(e.g., 3.3 volts). The accelerometer processing module 116 can
identify a frequency range, using the waveform voltage data 126,
corresponding to an output voltage (V.sub.out) waveform that spans
between the lower-end voltage (e.g., 2.2 volts) and the higher-end
voltage (e.g., 3.3 volts). For example, the accelerometer
processing module 116 can determine that the output voltage
(V.sub.out) of the lower-end voltage corresponds to a frequency of
650 Hz and the output voltage (V.sub.out) of the higher-end voltage
corresponds to a frequency of 1000 Hz. As a result, the
accelerometer processing module 116 can determine that the
vibrations 260 are caused by a sound having a frequency range
between 650 Hz and 1000 Hz.
[0032] The accelerometer processing module 116 can also access
sound identification data 128 from the memory 128 to identify
sounds using a specified frequency range. As a non-limiting
example, using the sound identification data 128, the accelerometer
processing module 116 can perform a look-up operation using the
650-1000 Hz frequency range to determine that firetrucks and
ambulances use sirens having the corresponding frequency range.
Using the above techniques, the accelerometer processing module 116
can determine that the measured vibrations 260 are caused by a fire
truck siren or an ambulance siren. Thus, in the above-described
example, the low frequency sound 190 external to the autonomous
vehicle 100 corresponds to a siren.
[0033] It should be appreciated that the above-described techniques
are not solely applicable to siren detection and can be used to
identify other low frequency sounds that cause the vibrations 260
proximate to the microphone unit 150. As a non-limiting example, in
other implementations, the accelerometer processing module 116 can
use the above techniques to determine that the measured vibrations
260 are caused by wind noise. In this implementation, a noise
cancellation signal can be generated to substantially reduce the
amount of wind noise detected by the microphone unit 150. For
example, the noise cancellation module 120 can generate a noise
cancellation signal 192 based on the sound frequencies associated
with the detected wind noise. The noise cancellation signal 192 can
be transmitted to an output device (not shown) to be played out
proximate to the microphone unit 150 in such a manner to reduce
(e.g., cancel out) the wind noise at the microphone unit 150.
[0034] In some implementations, the noise cancellation module 120
can perform post-processing noise cancellation. For example, after
noise is detected by the accelerometer 200, the microphones 151A-C,
or a combination thereof, the noise cancellation module 120 can
generate a noise cancellation signal (not shown) and add the noise
cancellation signal to the processed noise signal to reduce (e.g.,
cancel) noise.
[0035] According to one implementation, the accelerometer
processing module 116 is configured to monitor the electrical
signal 262 over a particular distance (e.g., twenty miles, thirty
miles, etc.) travelled by the autonomous vehicle 100 to determine
whether there is a faulty connection associated with the autonomous
vehicle 100. To illustrate, noise from a faulty connection may be
substantially continuous over a travelled distance, and thus, the
vibrations 260 caused by the faulty connection are typically
continuous. For example, if a screw is loose somewhere proximate to
the roof 102 of the autonomous vehicle 100 such that a rattling
noise is present, it is likely that the rattling noise will be
continuous while the autonomous vehicle 100 travels the particular
distance. As another example, if one or more of the microphones
151A-151C is detecting noise because of a bad microphone
connection, it is likely that the noise will be continuous while
the autonomous vehicle 100 travels the particular distance.
However, environmental noise surrounding the autonomous vehicle 100
may not be substantially continuous over a travelled distance
because the environment typically changes. For example, the
autonomous vehicle 100 can go in and out of tunnels during the
travelled distance, the autonomous vehicle 100 can drive by areas
that are playing loud music and then subsequently drive through
quiet areas, etc.
[0036] Thus, in response to a determination that the electrical
signal 262 indicates the waveform of the output voltage (V.sub.out)
is substantially continuous over the particular distance travelled
by the autonomous vehicle 100, the accelerometer processing module
116 can determine that the monitored vibrations 260 are caused by a
faulty connection associated with the autonomous vehicle 100.
However, in response to a determination that the electrical signal
262 indicates the waveform of the output voltage (V.sub.out) is not
substantially continuous over the particular distance travelled by
the autonomous vehicle 100, the accelerometer processing module 116
can determine that the monitored vibrations 260 are caused by
environmental noise.
[0037] According to some implementations, the accelerometer
processing module 116 determines the cause of the vibrations 260 in
response to a determination that one or more of the microphones
151A-151C are not satisfactorily detecting sounds that cause the
vibrations 260. For example, the microphone processing module 118
can monitor battery levels or power levels of the microphones
151A-151C. In response to a determination that the battery level of
one or more of the microphones 151A-151C falls below a threshold
such that the microphones 151A-151C cannot accurately detect
sounds, the accelerometer processing module 116 may use the
electrical signal 262 to identify sounds, as described above. Thus,
the accelerometer 200 and the accelerometer processing module 116
can be used in situations where one or more of the microphones
151A-151C lack power or have failed.
[0038] The techniques described with respect to FIGS. 1-2 enable
low frequency vibrations (e.g., sounds) to be detected using the
accelerometer 200 in the microphone unit 150 coupled to the roof
102 of the autonomous vehicle 100. As a result, the accelerometer
200 can detect wind noise, other environmental noise, a faulty
connection associated with the autonomous vehicle 100, etc.
Additionally, the accelerometer 200 can be used to detect noise in
scenarios where one or more of the microphones 151A-151C lack power
or have failed.
[0039] FIG. 3 depicts a diagram of microphones and an
accelerometer. In FIG. 3, the microphones 151A-151C and the
accelerometer 200 are coupled to the microphone board 157. The
accelerometer 200 is proximate to a center of the microphone board
157. The microphones 151A-151C are proximate to the edges of the
microphone board 157. In the illustrative embodiment of FIG. 3, the
microphone 151A is oriented in a first direction, the microphone
151B is oriented in a second direction that is 120 degrees from the
first direction, and the microphone 151C is oriented in a third
direction that is 120 degrees from the first direction and 120
degrees from the second direction.
[0040] FIG. 4 depicts a diagram of different roof locations to
couple a microphone unit in accordance with example embodiments. In
FIG. 4, the roof 102 of the autonomous vehicle 100 is depicted.
Different locations (e.g., Location A-Location E) on the roof 100
are depicted as potential places to couple microphone units, such
as the microphone unit 150. It should be understood that the
locations depicted in FIG. 4 are merely for illustrative purposes
and should not be construed as limiting.
[0041] The locations for the microphone units can be determined
based on detected wind speeds. For example, the microphone units
can be coupled to the roof 102 at locations with a relatively low
wind speed. Simulation data can be generated to detect the wind
speeds at different locations. For example, during a simulation,
sensors can be placed on the roof 102 of the autonomous vehicle 100
to detect the various wind speeds at different locations. According
to the non-limiting illustrative example in FIG. 4, Location A has
a wind speed of 35 meters per second (m/sec), Location B has a wind
speed of 30 m/sec, Location C has a wind speed of 10 m/sec,
Location D has a wind speed of 5 m/sec, and Location E has a wind
speed of 3 m/sec. Thus, according to the non-limiting illustrative
example in FIG. 4, Location E is a relatively good place to couple
a microphone unit, Location D is the second best place to couple a
microphone unit, Location C is the third best place to couple a
microphone unit, Location B is the next best place to couple a
microphone unit, and Location A is the worst place to couple a
microphone unit.
[0042] It should be understood that selected locations for the
microphone units can vary based on the structure of an autonomous
vehicle. Thus, different models of autonomous vehicles can have
different optimal locations for coupling the microphone units to
the roof.
III. Example Methods
[0043] FIG. 5 is a flowchart of a method 500 according to an
example embodiment. The method 500 can be performed by the
microphone unit 150 of FIG. 1 and the computing system 110 of FIG.
1.
[0044] The method 500 includes receiving, at a processor, an
electrical signal generated by an accelerometer, at 502. The
accelerometer is included in a microphone unit that is coupled to a
roof of an autonomous vehicle, and the electrical signal is
indicative of a waveform associated with vibrations proximate to
the microphone unit that are measured by the accelerometer. For
example, referring to FIG. 1, the processor 112 receives the
electrical signal 262 generated by the accelerometer 200. The
electrical signal 262 is indicative of the output voltage
(V.sub.out) waveform associated with the vibrations 260 proximate
to the microphone unit 150 that are measured by the accelerometer
200.
[0045] The method 500 also includes determining a cause of the
vibrations based on the electrical signal, at 504. For example,
referring to FIG. 1, the accelerometer processing module 116
determines the cause of the vibrations 260 based on the electrical
signal 262. According to one implementation, the cause of the
vibrations 260 is low frequency sounds 190 external to the
autonomous vehicle 100. For example, the low frequency sounds 190
can correspond to sirens.
[0046] According to one implementation of the method 500,
determining the cause of the vibrations 260 includes monitoring the
electrical signal 262 over a particular distance travelled by the
autonomous vehicle 100. The method 500 can also include determining
that the cause of the vibrations 260 is environmental noise in
response to a determination that the electrical signal 262
indicates the waveform (e.g., the output voltage (V.sub.out)
waveform) is not substantially continuous over the particular
distance travelled by the autonomous vehicle 100. The method 500
can also include determining that the cause of the vibrations 260
is a faulty connection associated with the autonomous vehicle 100
in response to a determination that the electrical signal 262
indicates the waveform (e.g., the output voltage (V.sub.out)
waveform) is substantially continuous over the particular distance
travelled by the autonomous vehicle 100.
[0047] According to one implementation, the method 500 can include
determining that the cause of the vibrations 260 is wind noise. In
this implementation, the method 500 can also include generating the
noise cancellation signal 192 based on the electrical signal 262 to
substantially reduce the wind noise.
[0048] According to one implementation, the method 500 can include
monitoring a signature associated with the accelerometer 200. The
signature can be based on the vibrations 260. The method 500 can
also include feeding the signature into a machine learning
algorithm for early defect detection associated with the
accelerometer 200. For example, the accelerometer processing module
116 can feed the signature into a machine learning algorithm to
predict when the accelerometer 200 is susceptible to defects. Based
on predictions, the computing system 110 can initiate a maintenance
scheduling request for improved safety and reduced cost.
[0049] The method 500 of FIG. 5 enables low frequency sounds to be
detected using the accelerometer 200 in the microphone unit 150
coupled to the roof 102 of the autonomous vehicle 100. As a result,
the accelerometer 200 can detect wind noise, environmental noise, a
faulty connection associated with the autonomous vehicle 100,
etc.
IV. Conclusion
[0050] The particular arrangements shown in the Figures should not
be viewed as limiting. It should be understood that other
embodiments may include more or less of each element shown in a
given Figure. Further, some of the illustrated elements may be
combined or omitted. Yet further, an illustrative embodiment may
include elements that are not illustrated in the Figures.
[0051] A step or block that represents a processing of information
can correspond to circuitry that can be configured to perform the
specific logical functions of a herein-described method or
technique. Alternatively or additionally, a step or block that
represents a processing of information can correspond to a module,
a segment, or a portion of program code (including related data).
The program code can include one or more instructions executable by
a processor for implementing specific logical functions or actions
in the method or technique. The program code and/or related data
can be stored on any type of computer readable medium such as a
storage device including a disk, hard drive, or other storage
medium.
[0052] The computer readable medium can also include non-transitory
computer readable media such as computer-readable media that store
data for short periods of time like register memory, processor
cache, and random access memory (RAM). The computer readable media
can also include non-transitory computer readable media that store
program code and/or data for longer periods of time. Thus, the
computer readable media may include secondary or persistent long
term storage, like read only memory (ROM), optical or magnetic
disks, compact-disc read only memory (CD-ROM), for example. The
computer readable media can also be any other volatile or
non-volatile storage systems. A computer readable medium can be
considered a computer readable storage medium, for example, or a
tangible storage device.
[0053] While various examples and embodiments have been disclosed,
other examples and embodiments will be apparent to those skilled in
the art. The various disclosed examples and embodiments are for
purposes of illustration and are not intended to be limiting, with
the true scope being indicated by the following claims.
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